Vy Nguyen

CALFP-MHC improves peptide-MHC binding prediction by using chemically grounded fingerprints and contrastive learning, achieving high AUC and PPV across diverse alleles and class imbalance.

This paper develops a machine learning model that integrates TCRβ and pHLA binding data to improve neoantigen prioritization in colorectal cancer, showing better performance than existing tools.

epiTCR-KDA improves TCR-peptide binding prediction by incorporating structural dihedral angles and knowledge distillation, achieving high AUC for both seen and unseen data.

scClassifR is an R package that accurately classifies cell types in scRNA-seq data, offering robustness in not-classifying unknown cells and reporting ambiguous assignments.

ReactomeGSA enables multi-omics, cross-species pathway analysis by integrating data from ExpressionAtlas and TCGA, revealing B cell-related NFkappaB activation differences in cancer.